Prenatal Exposure to Chemical Mixtures and Metabolic Syndrome Risk in Children

This cohort study analyzes prenatal exposures to various mixtures of endocrine-disrupting chemicals and their associations with metabolic syndrome and levels of proinflammatory proteins, amino acids, and glycerophospholipids among children in Europe.


eAppendix 1. Measurement of Metabolites and Protein Levels in Childhood
The description of the measurement of child metabolites and protein levels has been obtained from Maitre et al. 2022 3 and presented here verbatim.

Serum metabolites
"The AbsoluteIDQTM p180 kit was chosen for serum analysis as it is a standardised, targeted LCMS/MS assay, widely used for large-scale epidemiology studies and its inter-laboratory reproducibility has been demonstrated by several independent laboratories 4 .Serum samples were quantified using the AbsoluteIDQTM p180 kit following the manufacturer's protocol (User Manual UM_p180_AB_SCIEX_9, Biocrates Life Sciences AG) using LC-MS/MS; an Agilent HPLC 1100 liquid chromatography coupled to a SCIEX QTRAP 6500 triple quadrupole mass spectrometer.A full description of the HELIX metabolomics methods and data can be found elsewhere 5 .Briefly, the kit allows for the targeted analysis of 188 metabolites in the classes of amino acids, biogenic amines, acylcarnitines, glycerophospholipids, sphingolipids and sum of hexoses, covering a wide range of analytes and metabolic pathways in one targeted assay.The kit consists of a single sample processing procedure, with two separate analytical runs, a combination of liquid chromatography (LC) and flow injection analysis (FIA) coupled to tandem mass spectrometry (MS/MS).Isotopically labelled and chemically homologous internal standards were used for quantification.The AbsoluteIDQ p180 data of serum samples were acquired in 18 batches.Every analytical batch, in a 96-well plate format, included up to 76 randomised cohort samples.Also in every analytical batch, three sets of quality control samples were included, the NIST SRM 1950 plasma reference material (in 4 replicates), a commercial available serum QC material (CQC in 2 replicates, SeraLab, S-123-M-27485) and the QCs provided by the manufacturer in three concentration levels.The NIST SRM 1950 reference was used as the main quality control sample for the LC-MS/MS analysis.Coefficients of variation (CVs) for each metabolite were calculated based on the NIST SRM 1950 and also the limits of detection (LODs) were also used to assess the analytical performance of individual metabolites.Metabolite exclusion was based on a metabolite variable meeting two conditions: (1) CV of over 30% and (2) over 30% of the data are below LOD.Eleven out of the 188 serum metabolites detected were excluded as a result, leaving 177 serum metabolites to be used for further statistical analysis.The mean coefficient of variation across the 177 LC-MS/MS detected serum metabolites was 16%.We also excluded one HELIX sample, which was hemolyzed.Concentration levels were log2 transformed."

Urinary metabolites
"Two urine samples, representing last night-time and first morning voids, were collected on the evening and morning before the clinical examination, kept in a fridge and transported in a temperature-controlled environment, and aliquoted and frozen within 3 h of arrival at the clinics.They were subsequently pooled to generate a more representative sample of the last 24 h for metabolomic analysis.Urinary metabolic profiles were acquired using 1H NMR spectroscopy according to 5 .In brief onedimensional 600 MHz 1H NMR spectra of urine samples from each cohort were acquired on the same Bruker Avance III spectrometer operating at 14.1 Tesla within a period of 1 month.The spectrometer was equipped with a Bruker SampleJet system, and a 5mm broad-band inverse configuration probe maintained at 300K.Prior to analysis, cohort samples were randomised.Deuterated 3-(trimethylsilyl)-[2,2,3,3-d4]-propionic acid sodium salt (TSP) was used as internal reference.Aliquots of the study pooled quality control (QC) sample were used to monitor analytical performance throughout the run and were analysed at an interval of every 23 samples (i.e. 4 QC samples per well plate).The 1H NMR spectra were acquired using a standard onedimensional solvent suppression pulse sequence.Forty-four metabolites were identified and quantified as described (Supplementary Data 1H) 5 .The urinary NMR showed excellent analytical performance, the mean coefficient of variation across the 44 NMR detected urinary metabolites was 11%.Data was normalized using the median fold change normalization method30, which takes into account the distribution of relative levels of all 44 metabolites compared to the reference sample in determining the most probable dilution factor.An offset of ½ of the minimal value was applied and then concentration levels were expressed as log2".

Plasma proteins
"Plasma protein levels were assessed using the antibody-based multiplexed platform from Luminex.Three kits targeting 43 unique candidate proteins were selected (Thermo Fisher Scientifics, USA): Cytokines 30-plex (Catalog Number (CN): LHC6003M), Apoliprotein 5-plex (CN: LHP0001M) and Adipokine 15-plex (CN: LHC0017M).All samples were randomized and blocked by cohort prior measurement.For quantification, an 8-point calibration curve per plate was performed with protein standards provided in the Luminex kit and following procedures described by the vendor.Commercial heat inactivated, sterile-filtered plasma from human male AB plasma (Sigma-Aldrich, USA) was used as constant samples to control for intra-and interplate variability.Four control samples were added per plate.All samples, including controls, were diluted ½ for the 30-plex kit, ¼ for the 15-plex kit and 1/2500 for the 5-plex kit.Raw intensities obtained with the xMAP and Luminex system for each plasma sample were converted to pg/ml using the calculated standard curves of each plate and accounting for the dilutions made prior measurement.The percentages of coefficients of variation (CV%) for each protein by plate ranged from 3% to 36%.The limit of detection (LOD) and the lower and upper limit of quantification (LOQ1 and LOQ2, respectively) were estimated by plate, and then averaged.Only proteins with >30% of measurements in the linear range of quantification were kept in the database and the others were removed.Seven proteins were measured twice (in two different multiplex kits).We kept the measure with higher quality.The 36 proteins that passed the quality control criteria mentioned above were log2 transformed 5 .Then, the plate batch effect was corrected by subtracting the plate specific average for each protein minus the overall average of all plates for that protein.
After that, values below the LOQ1 and above the LOQ2 were imputed using a truncated normal distribution implemented in the truncdist R v1.0-2 package 6 .Twenty samples were excluded due to having ten or more proteins out of the linear range of quantification."Red and white nodes: variables associated to both exposure and outcomes (confounders).Including only those variables with the white nodes, the model is considered to be sufficiently adjusted for the estimation of the total effect (direct and indirect) between prenatal chemical mixtures and child MetS risk score.

eTable 3 .
Percentage of Missings in the Chemicals' Exposures and Covariates

Adjusted Associations Between Childhood Urine Metabolites and Child MetS Risk Score
Effect estimates were expressed per doubling of child urine metabolite levels.All models were adjusted for subcohort, parental country of birth, maternal age, maternal education level, maternal pre-pregnancy body mass index, parity, maternal smoking in pregnancy, and maternal fish intake in pregnancy.Abbreviations: FDR, false discovery rate; MetS, metabolic syndrome; SE, standard error © 2024 Güil-Oumrait N et al.JAMA Network Open.eTable 11.

Adjusted Associations Between Prenatal EDC Mixtures and Child Urine Metabolites Using BWQS
Beta estimates and 95% Credible intervals expressed as percent change of child urine metabolite levels per quartile increase in the exposure mixture.Significant associations with an FDR p-value below 0.05 are bolded.All models were adjusted for subcohort, parental country of birth, maternal age, maternal education level, maternal pre-pregnancy body mass index, parity, maternal smoking in pregnancy, and maternal fish intake in pregnancy.Abbreviations: BWQS, bayesian weighted quantile sum; EDC: endocrine disrupting chemicals; HMWPs, high-molecular weight phthalates; LCrI, lower 95% credible interval; LMWPs, lowmolecular-weight phthalate metabolites; OCs, organochlorines; PBDEs, polybrominated diethyl ethers; UCrI, upper 95% credible interval.eTable 14.